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Trajectory Clustering-Based Anomaly Detection in Indoor Human Movement

Human movement anomalies in indoor spaces commonly involve urgent situations, such as security threats, accidents, and fires. This paper proposes a two-phase framework for detecting indoor human trajectory anomalies based on density-based spatial clustering of applications with noise (DBSCAN). The f...

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Detalles Bibliográficos
Autores principales: Lan, Doi Thi, Yoon, Seokhoon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10058538/
https://www.ncbi.nlm.nih.gov/pubmed/36992030
http://dx.doi.org/10.3390/s23063318
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author Lan, Doi Thi
Yoon, Seokhoon
author_facet Lan, Doi Thi
Yoon, Seokhoon
author_sort Lan, Doi Thi
collection PubMed
description Human movement anomalies in indoor spaces commonly involve urgent situations, such as security threats, accidents, and fires. This paper proposes a two-phase framework for detecting indoor human trajectory anomalies based on density-based spatial clustering of applications with noise (DBSCAN). The first phase of the framework groups datasets into clusters. In the second phase, the abnormality of a new trajectory is checked. A new metric called the longest common sub-sequence using indoor walking distance and semantic label (LCSS_IS) is proposed to calculate the similarity between trajectories, extending from the longest common sub-sequence (LCSS). Moreover, a DBSCAN cluster validity index (DCVI) is proposed to improve the trajectory clustering performance. The DCVI is used to choose the epsilon parameter for DBSCAN. The proposed method is evaluated using two real trajectory datasets: MIT Badge and sCREEN. The experimental results show that the proposed method effectively detects human trajectory anomalies in indoor spaces. With the MIT Badge dataset, the proposed method achieves 89.03% in terms of F1-score for hypothesized anomalies and above 93% for all synthesized anomalies. In the sCREEN dataset, the proposed method also achieves impressive results in F1-score on synthesized anomalies: 89.92% for rare location visit anomalies (τ = 0.5) and 93.63% for other anomalies.
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spelling pubmed-100585382023-03-30 Trajectory Clustering-Based Anomaly Detection in Indoor Human Movement Lan, Doi Thi Yoon, Seokhoon Sensors (Basel) Article Human movement anomalies in indoor spaces commonly involve urgent situations, such as security threats, accidents, and fires. This paper proposes a two-phase framework for detecting indoor human trajectory anomalies based on density-based spatial clustering of applications with noise (DBSCAN). The first phase of the framework groups datasets into clusters. In the second phase, the abnormality of a new trajectory is checked. A new metric called the longest common sub-sequence using indoor walking distance and semantic label (LCSS_IS) is proposed to calculate the similarity between trajectories, extending from the longest common sub-sequence (LCSS). Moreover, a DBSCAN cluster validity index (DCVI) is proposed to improve the trajectory clustering performance. The DCVI is used to choose the epsilon parameter for DBSCAN. The proposed method is evaluated using two real trajectory datasets: MIT Badge and sCREEN. The experimental results show that the proposed method effectively detects human trajectory anomalies in indoor spaces. With the MIT Badge dataset, the proposed method achieves 89.03% in terms of F1-score for hypothesized anomalies and above 93% for all synthesized anomalies. In the sCREEN dataset, the proposed method also achieves impressive results in F1-score on synthesized anomalies: 89.92% for rare location visit anomalies (τ = 0.5) and 93.63% for other anomalies. MDPI 2023-03-21 /pmc/articles/PMC10058538/ /pubmed/36992030 http://dx.doi.org/10.3390/s23063318 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lan, Doi Thi
Yoon, Seokhoon
Trajectory Clustering-Based Anomaly Detection in Indoor Human Movement
title Trajectory Clustering-Based Anomaly Detection in Indoor Human Movement
title_full Trajectory Clustering-Based Anomaly Detection in Indoor Human Movement
title_fullStr Trajectory Clustering-Based Anomaly Detection in Indoor Human Movement
title_full_unstemmed Trajectory Clustering-Based Anomaly Detection in Indoor Human Movement
title_short Trajectory Clustering-Based Anomaly Detection in Indoor Human Movement
title_sort trajectory clustering-based anomaly detection in indoor human movement
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10058538/
https://www.ncbi.nlm.nih.gov/pubmed/36992030
http://dx.doi.org/10.3390/s23063318
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